creating AI Agent with Wisdom Graph

=== Cognitive Cycle 1 ===

🧠 Starting cognitive cycle...

🔍 PERCEPTION SPACE
Environment Analysis:
- Room type: indoor_room
- Objects detected: 3
- Lighting condition: natural_daylight

Self State Analysis:
- Current position: (0, 0, 0)
- Energy level: 80/100
- Knowledge level: 60/100

Distance Analysis:
- Distance to flower_vase: 28.3 units
- Distance to clothing_rack: 113.1 units
- Distance to window: 122.5 units

📋 PLANNING SPACE
Available Plans:

- Move directly towards target
  Energy required: 5
  Energy gain: 0
  Success probability: 0.8

- Explore environment while moving
  Energy required: 8
  Energy gain: 0
  Success probability: 0.9
  Knowledge gain: 5

- Take energy-efficient path
  Energy required: 3
  Energy gain: 0
  Success probability: 0.7

- Rest to recover energy
  Energy required: 0
  Energy gain: 10
  Success probability: 1.0

🤔 REASONING SPACE
Decision Analysis:
- Selected plan: Explore environment while moving
- Confidence score: 0.79
- Energy state: sufficient

⚡ ACTION SPACE
Explored and moved to: (1.0, 1.0, 1.0)
Analyzed environment and gathered data

Action Results:
- Energy level: 72/100
- Knowledge level: 65/100
- Current position: (1.0, 1.0, 1.0)

📊 Agent Status:
Position: (1.0, 1.0, 1.0)
Energy: 72
Knowledge: 65

=== Cognitive Cycle 2 ===

🧠 Starting cognitive cycle...

🔍 PERCEPTION SPACE
Environment Analysis:
- Room type: indoor_room
- Objects detected: 3
- Lighting condition: natural_daylight

Self State Analysis:
- Current position: (1.0, 1.0, 1.0)
- Energy level: 72/100
- Knowledge level: 65/100

Distance Analysis:
- Distance to flower_vase: 26.9 units
- Distance to clothing_rack: 111.7 units
- Distance to window: 120.8 units

📋 PLANNING SPACE
Available Plans:

- Move directly towards target
  Energy required: 5
  Energy gain: 0
  Success probability: 0.8

- Explore environment while moving
  Energy required: 8
  Energy gain: 0
  Success probability: 0.9
  Knowledge gain: 5

- Take energy-efficient path
  Energy required: 3
  Energy gain: 0
  Success probability: 0.7

- Rest to recover energy
  Energy required: 0
  Energy gain: 10
  Success probability: 1.0

🤔 REASONING SPACE
Decision Analysis:
- Selected plan: Explore environment while moving
- Confidence score: 0.79
- Energy state: sufficient

⚡ ACTION SPACE
Explored and moved to: (2.0, 2.0, 2.0)
Analyzed environment and gathered data

Action Results:
- Energy level: 64/100
- Knowledge level: 70/100
- Current position: (2.0, 2.0, 2.0)

📊 Agent Status:
Position: (2.0, 2.0, 2.0)
Energy: 64
Knowledge: 70

=== Cognitive Cycle 3 ===

🧠 Starting cognitive cycle...

🔍 PERCEPTION SPACE
Environment Analysis:
- Room type: indoor_room
- Objects detected: 3
- Lighting condition: natural_daylight

Self State Analysis:
- Current position: (2.0, 2.0, 2.0)
- Energy level: 64/100
- Knowledge level: 70/100

Distance Analysis:
- Distance to flower_vase: 25.5 units
- Distance to clothing_rack: 110.3 units
- Distance to window: 119.2 units

📋 PLANNING SPACE
Available Plans:

- Move directly towards target
  Energy required: 5
  Energy gain: 0
  Success probability: 0.8

- Explore environment while moving
  Energy required: 8
  Energy gain: 0
  Success probability: 0.9
  Knowledge gain: 5

- Take energy-efficient path
  Energy required: 3
  Energy gain: 0
  Success probability: 0.7

- Rest to recover energy
  Energy required: 0
  Energy gain: 10
  Success probability: 1.0

🤔 REASONING SPACE
Decision Analysis:
- Selected plan: Explore environment while moving
- Confidence score: 0.79
- Energy state: sufficient

⚡ ACTION SPACE
Explored and moved to: (3.0, 3.0, 3.0)
Analyzed environment and gathered data

Action Results:
- Energy level: 56/100
- Knowledge level: 75/100
- Current position: (3.0, 3.0, 3.0)

📊 Agent Status:
Position: (3.0, 3.0, 3.0)
Energy: 56
Knowledge: 75